Economic_DER_Integration :

Economical demand-side management with distributed energy resources

INTRODUCTION:

Demand side management is an important aspect of managing the energy system. The process involves matching the amount of energy being produced with the amount of energy being consumed. A difficult issue because there is not enough storage available and financially feasible. Time dealing with the current situation. It is now necessary to address the peak demand because of the expensive cost of the immediate market and traffic in the delivery system may need to reassess their current approach and consider implementing new strategies to address these emerging challenges. Indicates there is a growing global recognition and admiration for load flexibility. The term used to describe this concept is called Demand Response (DR), which aims to raise the level of the system to make it more efficient and increase flexibility. Demand refers to the quantity of a good or service that consumers are willing and able to purchase at a given price and within a specific time period. System is no longer considered acceptable. The system is in danger and the market price is at risk in the near future.

The competition for supplying peak load is intense. Demand response refers to the practice of adjusting electricity consumption in response to changes in power supply or pricing. Demand response (DR) refers to the procedure of altering electricity usage (Change their behaviour). The introduction of smart grid technology has made it possible to put into action. The text mentions the use of advanced DR techniques. DR aids in the process of integration. Renewable distributed energy resources can be changed by making modifications to the system. Enhance the energy efficiency and dependability by enabling the incorporation of profiles. Demand Response is advantageous not only for the distribution utilities, but also the consumers. One-way individuals can reduce their electricity expenses is by engaging in demand response. According to the load profile, consumers can be classified as Residential, Commercial and Industrial. The quantum of the load is quite different among these sectors. Moreover, according to the type of control, DR can be classified into two categories, centralized control and distributed control. Communication infrastructure and decision centre are the major difference between these two [12]. In centralized control, DR aggregator takes the decision and communicates it to the consumers individually. There is no communication link between the consumers. In decentralized control, only DR aggregator communicates the price signal to the consumers and the decision is taken by the consumer. There is a communication infrastructure between consumers. Decentralized control can have multiple objectives such as voltage balance, frequency regulation and dynamic stability etc. to improve the health of the grid.

However, power system has made significant technical advancement, still distribution system is struggling to address the growing demand. The energy demand is increasing in all the sectors as residential, industrial and commercial. The residential sector has significant energy conservation potential as averaged worldwide; the residential sector consumes approximately 30% of the total energy consumption [13]. Therefore, it is required to focus on techniques to decrease energy consumption in residential sector. The basic idea behind designing a good Demand Response strategy is to give certain incentives to the electricity consumers to alter their consumption pattern which is beneficial not only to the consumers from the financial perspective but also to the electricity provider and overall reliability of the power system [14]. Various pricing mechanism for DR schemes such as Time of Use (TOI), Real Time Pricing (RTP) and Critical Peak Pricing (CPP) have been developed which addresses the above objectives [8].

Key Challenges:

One of the main difficulties in carrying out successful DSM (Demand Side Management) programs is finding appropriate customers who are able to engage in demand response initiatives. These individuals, commonly known as "DSM candidates," are essential in meeting high demand periods and decreasing energy usage during expensive hours.

Approach to the Problem:

RESULTS AND DISCUSSION:

During the investigation, 300 Household customers were analysed with data available at 30-minute intervals over a one-year duration. Three key technical parameters were considered to identify target customers: Fig 9 shows a probability density map illustrating the average daily consumption by consumers. That has an average of 15.27 kWh and a standard deviation of 6.43 kWh.

Requirements💻 :

Ensure you have the following dependencies installed:

• Python (version 3.9.x || 3.12.x)
• IDE: VS-CODE or collab
• Virtual-environment(venv)
• Other dependencies (refer to the requirements.txt)

You can install the required Python packages using:

pip install -r requirements.txt

Setup 💿:

• Clone the repository:

git clone https://github.com/SINGHxTUSHAR/Economic_DER_Integration.git
cd Economic_DER_Integration

• Create a virtual environment (optional but recommended):

python -m venv venv

• Activate the virtual environment:
  • On Windows:

  venv\Scripts\activate

  • On macOS/Linux:

  source venv/bin/activate

Contributing 📌:

If you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!

Suggestion 🚀:

If you have any suggestions for me related to this project, feel free to contact me at tusharsinghrawat.delhi@gmail.com or LinkedIn.

CONCLUSIONS AND SCOPE FOR FUTURE WORK:

• It is a great opportunity for utility firms to use the load profiling data for designing Demand Response strategies, which enable bespoke DR interventions targeting certain consumer groups with specific behavior patterns.

• The k-means algorithm has exposed six distinct natural clusters within the load profile dataset. Each cluster has unique characteristics that are marked by changes in the shape of profiles, hence making it possible to understand subtle differences in how people behave. Moreover, the examination enabled the graphical representation of various vital customer details such as demand fluctuation; weekday versus weekend operation; susceptibility to climatic parameters, and simultaneous peak loads.

• This simplifies energy consumption awareness among customers. By using this approach, 70 probable customers out of 300 have been identified for focusing on DR schemes. With their participation, these chosen clients cut down the system’s peak demand by an impressive 17% with PV use and thus revealing practical advantages associated with grid-optimized operations using evidence-based techniques resulting in better efficiency overall. Moving forward, our plan entails further refining and enhancing our models, as well as exploring alternative techniques to enhance their performance.

• We aim to continually improve the accuracy and reliability of our forecasting models for time series analysis in the future.

License 📝:

This project is licensed under the MIT License - see the LICENSE file for details.